So sánh phương pháp

	Thiết kế Nghiên cứu Sự kiện Động	Khác biệt trong Khác biệt Động
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2021 (canonical treatment); practice since 1990s)	2021
Người khởi xướng	Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics	Callaway & Sant'Anna; Sun & Abraham
Loại	Quasi-experimental / causal inference	Causal inference / quasi-experimental
Công trình gốc	Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Tên gọi khác	dynamic DiD, lead-lag event study, relative-time event study, event-time regression	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Liên quan	3	4
Tóm tắt	The dynamic event study design extends the standard difference-in-differences framework by estimating treatment effects at each period before and after the event, rather than collapsing everything into a single post-treatment coefficient. By plotting lead and lag coefficients against relative event time, researchers can simultaneously test for pre-existing trends and trace how the causal effect evolves over multiple post-treatment periods.	Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time.